table lens from papers 1 and 2 by tichomir tenev, ramana rao, and stuart k. card
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![Page 1: Table Lens From papers 1 and 2 By Tichomir Tenev, Ramana Rao, and Stuart K. Card](https://reader033.vdocument.in/reader033/viewer/2022052603/56649d5d5503460f94a3b547/html5/thumbnails/1.jpg)
Table Lens
From papers 1 and 2
By Tichomir Tenev, Ramana Rao, and Stuart K. Card
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Overview
Uses focus+Context apporach Context elements are represented graphically Focus elements have text and graphic
display
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Advantages
Increases viewable portion of table by 100 times
Ease of Navigation Ease of Exploration
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Table Lens Focal Technique
Mutates layout of table Does not bend any rows or columns
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Distortion Function Framework DOI function: item -> value. Value indicates
level of interest DOI function controls how available space is
allocated among items
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DOI in Table lens
DOI maps cell address to interest level 2 of them, one for each dimension
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Manipulation of Focus Operations Zoom- changes amount of space to focal
area Adjust- changes amount of contents viewed
within focus area Slide- changes location of focus area within
the context
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User manipulation
Clicking at upper left corner- zooms all cells Touching any region in context will slide
current focus to that location Grasping focus slides focus to that location
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Results
Apply data to baseball stats of 323 rows by 23 columns (7429 cells)
Display whole table on screen at one time
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Paper #2 Design 1 Nesting Focal Levels Space allocated to each element is
dependent on the focal level of element 2 foci, Primary focus always inside region of
secondary focus 2ndary focus used for coarse navigation Primary used for finer navigation
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Design II Controlling focal spans Space allocated per data element dependent
on focus level and parameter specified by user
Primary focus elements may vary in size Spatial map at any time depends on History
of user interaction
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Conclusion
Felt design 2 was the better design.
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Disadvantage
Works only for data tables which have have <= number of entries as pixel rows and each column has enough pixels wide to accommodate variables.
Paper #2 discusses how to improve it
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Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational DatabasesChris Stolte and Pat Hanrahan
Standford University
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Polaris
Interactive exploration of large multi-dimensional databases
Expressive set of graphical displays Uses tables to organize multiple graphs on a
display
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Relational databases
Each row in table = basic entity (tuple) Each column represents a field Fields can be ordinal, or quantitative
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Visual Specification
Is the configuration of the fields of the tables on shelves
User does this by dragging and dropping fields onto shelves
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Visual Specification
Mapping of data sources to layers # of rows, columns, and layers, and relative
order Selection of tuples from the database Grouping of data within a pane Type of graphic displayed in each pane Mapping of data fields with retinal properties
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Table Algebra
Used to specify table configurations. Dragging and dropping implicitly does it
Operands are the names of the ordinal and quantitative fields of database
Operators (concatenation, cross, nest)
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Types of Graphics (Ordinal- Ordinal) Axis variables are independent of each other
R represents the fields encoded in the retinal properties of the marks
Following slide shows sales and margin as a function of product type, month and state for items sold by coffee chain
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Ordinal-Quantitative Graphics Bar charts, dot plots, Gantt chart Quantitative variable is dependent of ordinal
variable
Figure 6c shows a case where a matrix of bar charts is used to study several functions of the independent variables product and month
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Quantitative-Quantitative Graphics Discover causal relationships between the
two quantitative variables.
Figure 3e shows how flight scheduling varies with the region of the country the flight originated.
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Visual mappings
Encoding different fields of the data to retinal properties
Shape, Size, Orientation, Color Used in the ordinal to ordinal example
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Generating Database Queries
1. Selecting the Records
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Generating Database Queries
2. Partitioning the records into pains Putting retrieved records in their corresponding
pane
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Generating Database Queries
3. Transforming Records within the Panes If aggregation, it is done here
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Results
Cut expenses for a national coffee store Create table of scatterplots showing
relationship between marketing costs and profit (Figure 6a)
Notice trend; certain products have high marketing costs with no or little profit
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Results
Used linked displays to determine that in New York several products are offering very little return despite high costs
Creates bar chart for products in New York
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Future Work
Exploring interaction techniques for navigating hierarchical structures of mulit-dim databases
Use selected mark in one display as the data input to another